rstudio regression

8.3 Regression Diagnosis> Fit> par (mfrow=c (2,2))> Plot (FIT)To understand these graphs, let's review the statistical assumptions of OLS regression.Mouth normality when the Predictor value is fixed, the dependent variable is normally distributed, and the residual value should also be a normal distribution with a mean of 0. A normal q-q graph (normal q-q, upper right) is a probability map of the normalized residuals under the corresponding value of th

Classification and logistic regression (classification and logistic regression)Http://www.cnblogs.com/czdbest/p/5768467.htmlGeneralized linear model (generalized Linear Models)Http://www.cnblogs.com/czdbest/p/5769326.htmlGenerate Learning Algorithm (generative learning algorithms)Http://www.cnblogs.com/czdbest/p/5771500.htmlClassification and logistic regression

First, Logistic regression In the linear regression of machine learning, we can use the gradient descent method to get a mapping function hθ (x) H_\theta (x) to come and go close to the sample point, this function is a prediction of the continuous value. While logistic regression is an algorithm to solve the classification problem, we can get a mapping function

Mathematics in machine learning (1)-Regression (regression), gradient descent (gradient descent)Copyright Notice:This article is owned by Leftnoteasy and published in Http://leftnoteasy.cnblogs.com. If reproduced, please specify the source, without the consent of the author to use this article for commercial purposes, will be held accountable for its legal responsibility.Objective:Last wrote a about Bayesia

Supervised learningFor a house price forecasting system, the area and price of the room are given, and the axes are plotted by area and price, and each point is drawn.Defining symbols:\ (x_{(i)}\) represents an input feature \ (x\).\ (y_{(i)}\) represents an output target \ (y\).\ ((x_{(i)},y_{(i)}) represents a training sample.\ (\left\{(x_{(i)},y_{(i)}), i=1,\dots,m\right\}\) represents a sample of M, also known as a training set.Superscript \ ((i) \) represents the index of the sample in the

Tree regression for cart algorithm:Each node returned is finally a final determined average.#coding:utf-8importnumpyasnp# Loading file Data defloaddataset (fileName): #general functiontoparsetab-delimitedfloats dataMat=[] #assume NBSP;LASTNBSP;COLUMNNBSP;ISNBSP;TARGETNBSP;VALUENBSP;NBSP;NBSP;NBSP;FR =open (FileName) forlineinfr.readlines (): curline=line.strip (). Split (' \ t ') fltline=map (float,curline) #map allelementstofloat () datamat.appen

Regression Model performance evaluation series 1-QQ chart, regression model evaluation 1-qq(Erbqi) the QQ plot is the Quantile-Quantile diagram, that is, the Quantile-Quantile diagram. A simple understanding is to plot the values of the two same Quantile distributions into points (x, y; if the two distributions are very close, the vertex (x, y) will be distributed near the y = x straight line; otherwise, no

This log is indeed a trigger. I am not familiar with R, but it is required by the experiment, so I just learned it. We found that, whether it's countless tutorials on the Internet or examples in books, when talking about logistic regression, we will give a simple function and a description of the output results. I have never been clear about several things: 1. How to Use training data to train the model and then verify the test data (the test data and

1-Questions raised 2-Logistic regression 3-Theoretical derivation 4-python/spark implementation1 #-*-coding:utf-8-*-2 fromPysparkImportSparkcontext3 fromMathImport*4 5theta = [0, 0, 0]#Initial theta Value6Alpha = 0.001#Learning Rate7 8 definner (x, y):9 returnSUM ([i*j forI,jinchzip (x, y)])Ten One deffunc (LST): AH = (1 + exp (-inner (LST, theta))) * * (-1) - returnMapLambdax: (H-lst[-1]) * x, Lst[:-1]) - the -s

distributed.This series mainly want to be able to use mathematics to describe machine learning, want to learn machine learning, first of all to understand the mathematical significance, not necessarily to be able to easily and freely deduce the middle formula, but at least to know these formulas, or read some related papers can not read, This series will focus on the mathematical description of machine learning, which will cover but not necessarily limited to

The classification problem is similar to the linear regression problem, but in the classification problem, we predict that the Y value is contained in a small discrete data set. First, to recognize the two-dollar classification (binary classification), in the two-dollar category, the value of Y can only be 0 and 1. For example, we want to do a spam classifier, the message is the characteristics, and for Y, when it is 1 spam, 0 indicates that the messa

Kf=read.csv (' D:/kf.csv ') # Read recovery dataKfSl=as.matrix (Kf[,1:3]) #生成生理指标矩阵Xl=as.matrix (Kf[,4:6]) #生成训练指标矩阵X=slXY=xlYX0=scale (x)X0Y0=scale (y)Y0M=t (x0)%*%y0%*%t (y0)%*%x0MEigen (M)W1=eigen (m) $vectors [, 1]V1=t (y0)%*%x0%*%w1/sqrt (As.matrix (Eigen (m) $values) [1,])V1T1=X0%*%W1 #第一对潜变量得分向量T1 # above for the first step (1) to extract the first pair of two variables group, and make it the most relevant.U1=y0%*%v1U1 #第一对潜变量得分向量Library ("PRACMA")Α1=INV (t (t1)%*%t1)%*%t (T1)%*%x0 #也可由t

first, the basic principle logical regression and linear regression The principles of Logistic regression and linear regression are similar, and, in my own understanding, can be described simply as such a process: (1) Find a suitable predictive function (called Hypothesis in the public class of Andrew NG), which is g

4. Lasso regression and Ridge (Ridge) regressionPDF version Download address: https://pan.baidu.com/s/1i5JtT9j HTML version download address: Https://pan.baidu.com/s/1kV0YVqv LASSO from 1996 Robert Tibshirani first proposed that the full name least absolute shrinkage and selection operator Ridge regression, also known as Ridge regression, Tychonoff regularization

Python learning notes logistic regression and python learning notes Regression 1 #-*-coding: UTF-8-*-2 "3 Created on Wed Apr 22 17:39:19 2015 4 5 @ author: 90 Zeng 6 "7 8 import numpy 9 import theano10 import theano. tensor as T11 import matplotlib. pyplot as plt12 rng = numpy. random13 N = 400 #400 samples 14 feats = 784 # dimension of each sample 15 D = (rng. randn (N, feats), rng. randint (size = N, low

Logical regression model, its own understanding of logic is equivalent to right and wrong, that is only 0, 1 of the case. This is what I saw in a great God, https://blog.csdn.net/zouxy09/article/details/20319673. The logistic regression model is used to classify, and it is possible to know which factors are dominant so that an event can be predicted. I downloaded from the internet a 2017 high school scienc

Learning notes TF024: TensorFlow achieves Softmax Regression (Regression) Recognition of handwritten numbers, tf024softmax TensorFlow implements Softmax Regression (Regression) to recognize handwritten numbers. MNIST (Mixed National Institute of Standards and Technology database), simple machine vision dataset, 28x28 p

, the picture of the training data set is Mnist.train.images, and the label for the training dataset is mnist.train.labels. Each picture contains 28 pixels X28 pixels. We can use a number array to represent this image: We expand this array into a vector with a length of 28x28 = 784. How to expand this array (the order between the numbers) is unimportant, as long as the individual images are expanded in the same way. From this perspective, a picture of the Mnist dataset is a point within a 784-d

1 Basic Concepts 1) definition Gradient Descent method is to use negative gradient direction to determine the new search direction of each iteration, so that each iteration can reduce the objective function to be optimized gradually . The gradient descent method is the steepest descent method under the 2 norm. A simple form of the steepest descent method is: X (k+1) =x (k)-a*g (k), where a is called the learning rate, which can be a smaller constant. G (k) is the gradient of X (k). The gradient

transferred from: Http://www.cnblogs.com/LeftNotEasy Author: leftnoteasy regression and gradient descent: Regression in mathematics is given a set of points, can be used to fit a curve, if the curve is a straight line, that is called linear regression, if the curve is a two-time curve, is called two regression,